I2V is trained on the fp16 I2v model.

Edit: I initially started training this as an I2V model because I get better quality using those models in T2V generation. I trained the T2V version on a whim at the same time to test Vast AI out. The two I2V samples were just random images from civitai that I quickly threw two prompts at. I don't typically generate with starting images so I cannot speak to it's performance.

I've attached the captions from the dataset to the t2v - low noise model in the optional drop down box if you'd like to check them out. Updated the model description, but the thought process was to keep them simple and focus on the movement.

All the metadata is saved in the samples. Just download a video and drag and drop it into ComfyUI.

worked best with a MoE sampler workflow. I tried a few gens with three samplers, but the motion was a little unhinged.

Captions are included in the zip file attached to the T2V low noise model.

Used my MacBook to trim clips down to six seconds and crop them to perfect squares in the Photo’s app. Converted all the clips to MP4 by rename the extension.

Ran the clips through FFMPEG to:

1. Convert the resolution to 320x320

2. Convert the frame rate to 16 FPS

3. Trim the videos down to 81 frames (this step may be unnecessary, I believe AI toolkit can do this automatically. But it only takes a minute or two for FFMPEG to do it)

The commands can be found through google, the AI should spit out the code as the first result. For step three the code needs to be changed from c:v to c:a if I remember correctly.

I included the captions in the T2V as an optional download. Start with trigger word or multiple words. Focus on the movement and describe the trigger word with natural language. Tried to keep it simple. Blonde woman, sultry brunette… etc to vaguely identity the subject. Tried to include multiple synonyms like penis and cock, tits and boobs, etc in the same caption so the Lora should respond to natural language. Sometimes included clothing in the captions, but I don’t think wan needs that information for motion.

Used AI toolkit. Used a PRO 6000 from runpod.

Transformer Quant: none

Low vram: off

Save every: dataset x 10

Rank: 32

Steps: dataset x 100

Timestep: linear

Use EMA

Decay default

Everything else is default. Turned off sampling.

Edit the advanced config resolution from 256 to 320

I believe it took 10 hours.

Follow up edit: I had made one Lora based on a 256, 81 frame dataset that was a little bigger and few more triggers but at rank 16. It would do the movements well but things would morph together. The second time around I made sure each clip started with everything important was in focus and visible in the initial frame. If the clip was focused on the blowjob trigger it would start with it in the woman’s mouth. Handjob would clearly show the hands at the base or middle with the tip clearly sticking out. So on and so on.. some clips feature multiple triggers. The first trigger is the initial movement, followed by a second (or even third) if applicable.

No interest. To make a Lora like this would cost about 2 grand in my time.

So when generating with dr34mlay and other similar Lora’s I noticed I got better better results using the I2V versions with the 1022 version of the lightx2v Lora. No idea why it works, but the generations always looked more realistic without the AI skin. The 260412 version posted on Kijai’s hugging face would give the best handle motion even better. 1022 would still give good results, but worked better with cumshot Lora’s. So eventually it just became my go-to.

https://limewire.com/d/VHBiF#erDMPjTBM8 here's is 6 comparison videos. three use dr34mlay v2 t2v with the three different lightx2v loras and three use dr34mlay v2 i2v with three different lightx2v loras. Same settings for everything else.